In unmanned aerial vehicle (UAV) surveillance and target tracking operations, persistent and robust target tracking/re-acquisition/re-identification is needed. However, in urban environments, target loss situations are often confronted due to partial or total occlusion by buildings, bridges, or other landmarks. Existing techniques for reacquisition of a target may analyze a motion of a target on a road, for example, and try to reacquire a target location using an assumption of linear or close to linear target trajectories. Other existing techniques may perform vehicle fingerprinting using line segment features of the tracked vehicles by determining an orientation of the vehicle (e.g., by aligning collection of line features from the vehicle into a rectangular cuboid), and estimates matching using a likelihood method for line segments.
Existing techniques may not be applicable in all operations. For examples, trajectory matching may not apply to objects that have dynamic trajectories or trajectories that do not follow roads or landmarks. Further, clear image quality and large target sizes may be required in order to extract a sufficient number of line features from vehicles, however, in practice, it can be difficult to acquire clear and large target images at all times from the UAVs.